UNMANNED AERIAL VEHICLE LASER SCANNING FOR EROSION MONITORING IN ALPINE GRASSLAND

Abstract. With this contribution we assess the potential of unmanned aerial vehicle (UAV) based laser scanning for monitoring shallow erosion in Alpine grassland. A 3D point cloud has been acquired by unmanned aerial vehicle laser scanning (ULS) at a test site in the subalpine/alpine elevation zone of the Dolomites (South Tyrol, Italy). To assess its accuracy, this point cloud is compared with (i) differential global navigation satellite system (GNSS) reference measurements and (ii) a terrestrial laser scanning (TLS) point cloud. The ULS point cloud and an airborne laser scanning (ALS) point cloud are rasterized into digital surface models (DSMs) and, as a proof-of-concept for erosion quantification, we calculate the elevation difference between the ULS DSM from 2018 and the ALS DSM from 2010. For contiguous spatial objects of elevation change, the volumetric difference is calculated and a land cover class (bare earth, grassland, trees), derived from the ULS reflectance and RGB colour, is assigned to each change object. In this test, the accuracy and density of the ALS point cloud is mainly limiting the detection of geomorphological changes. Nevertheless, the plausibility of the results is confirmed by geomorphological interpretation and documentation in the field. A total eroded volume of 672&thinsp;m3 is estimated for the test site (48&thinsp;ha). Such volumetric estimates of erosion over multiple years are a key information for improving sustainable soil management. Based on this proof-of-concept and the accuracy analysis, we conclude that repeated ULS campaigns are a well-suited tool for erosion monitoring in Alpine grassland.

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Compressed pseudo-SLAM: pseudorange-integrated compressed simultaneous localisation and mapping for unmanned aerial vehicle navigation

Journal of Navigation ◽

10.1017/s037346332100031x ◽

2021 ◽

pp. 1-13

Author(s):

Jonghyuk Kim ◽

Jose Guivant ◽

Martin L. Sollie ◽

Torleiv H. Bryne ◽

Tor Arne Johansen

Keyword(s):

Unmanned Aerial Vehicle ◽

Computational Cost ◽

Satellite System ◽

Measurement Unit ◽

Electronic Systems ◽

Computationally Efficient ◽

Global Correlation ◽

Aerial Vehicle ◽

Correlation Information ◽

Global Navigation Satellite

Abstract This paper addresses the fusion of the pseudorange/pseudorange rate observations from the global navigation satellite system and the inertial–visual simultaneous localisation and mapping (SLAM) to achieve reliable navigation of unmanned aerial vehicles. This work extends the previous work on a simulation-based study [Kim et al. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localisation and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36] to a real-flight dataset collected from a fixed-wing unmanned aerial vehicle platform. The dataset consists of measurements from visual landmarks, an inertial measurement unit, and pseudorange and pseudorange rates. We propose a novel all-source navigation filter, termed a compressed pseudo-SLAM, which can seamlessly integrate all available information in a computationally efficient way. In this framework, a local map is dynamically defined around the vehicle, updating the vehicle and local landmark states within the region. A global map includes the rest of the landmarks and is updated at a much lower rate by accumulating (or compressing) the local-to-global correlation information within the filter. It will show that the horizontal navigation error is effectively constrained with one satellite vehicle and one landmark observation. The computational cost will be analysed, demonstrating the efficiency of the method.

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Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

Canadian Journal of Forest Research ◽

10.1139/cjfr-2019-0418 ◽

2020 ◽

Vol 50 (10) ◽

pp. 1012-1024

Author(s):

Meimei Wang ◽

Jiayuan Lin

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Laser Scanning ◽

Tree Height ◽

Absolute Error ◽

Coefficient Of Determination ◽

Surface Model ◽

Individual Tree ◽

Aerial Vehicle ◽

The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

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Processing Chain for Estimation of Tree Diameter from GNSS-IMU-Based Mobile Laser Scanning Data

Remote Sensing ◽

10.3390/rs11060615 ◽

2019 ◽

Vol 11 (6) ◽

pp. 615 ◽

Cited By ~ 8

Author(s):

Juraj Čerňava ◽

Martin Mokroš ◽

Ján Tuček ◽

Michal Antal ◽

Zuzana Slatkovská

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Forest Canopy ◽

Point Clouds ◽

Satellite System ◽

Forest Road ◽

Forest Mapping ◽

Fitting Method ◽

Progressive Technology ◽

Global Navigation Satellite

Mobile laser scanning (MLS) is a progressive technology that has already demonstrated its ability to provide highly accurate measurements of road networks. Mobile innovation of the laser scanning has also found its use in forest mapping over the last decade. In most cases, existing methods for forest data acquisition using MLS result in misaligned scenes of the forest, scanned from different views appearing in one point cloud. These difficulties are caused mainly by forest canopy blocking the global navigation satellite system (GNSS) signal and limited access to the forest. In this study, we propose an approach to the processing of MLS data of forest scanned from different views with two mobile laser scanners under heavy canopy. Data from two scanners, as part of the mobile mapping system (MMS) Riegl VMX-250, were acquired by scanning from five parallel skid trails that are connected to the forest road. Misaligned scenes of the forest acquired from different views were successfully extracted from the raw MLS point cloud using GNSS time based clustering. At first, point clouds with correctly aligned sets of ground points were generated using this method. The loss of points after the clustering amounted to 33.48%. Extracted point clouds were then reduced to 1.15 m thick horizontal slices, and tree stems were detected. Point clusters from individual stems were grouped based on the diameter and mean GNSS time of the cluster acquisition. Horizontal overlap was calculated for the clusters from individual stems, and sufficiently overlapping clusters were aligned using the OPALS ICP module. An average misalignment of 7.2 mm was observed for the aligned point clusters. A 5-cm thick horizontal slice of the aligned point cloud was used for estimation of the stem diameter at breast height (DBH). DBH was estimated using a simple circle-fitting method with a root-mean-square error of 3.06 cm. The methods presented in this study have the potential to process MLS data acquired under heavy forest canopy with any commercial MMS.

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KONTRIBUSI GLOBAL NAVIGATION SATELLITE SYSTEM PADA PENENTUAN TITIK KONTROL UDARA DI METODE FOTOGRAMETRI UNMANNED AERIAL VEHICLE UNTUK PEMETAAN SKALA BESAR DI INDONESIA

Seminar Nasional Geomatika ◽

10.24895/sng.2020.0-0.1202 ◽

2021 ◽

pp. 867

Author(s):

Irwan Gumilar ◽

Deni Suwardhi ◽

Irfan Budaya ◽

Brian Bramanto ◽

Kamal Nur Fauzan

Keyword(s):

Unmanned Aerial Vehicle ◽

Global Navigation Satellite System ◽

Satellite System ◽

Navigation Satellite ◽

Mean Square ◽

Post Processing ◽

Check Points ◽

Aerial Vehicle ◽

Global Navigation ◽

Global Navigation Satellite

Indonesia saat ini sedang melakukan pemetaan skala besar secara masif. Salah satu metode yang digunakan pada pemetaan skala besar tersebut adalah dengan menggunakan teknik fotogrametri berbasiskan Unmanned Aerial Vehicle (UAV). Saat ini, metode penentuan titik kontrol udara dengan menggunakan Global Navigation Satellite System (GNSS) banyak dilakukan untuk memimalisir jumlah titik kontrol tanah tanpa mengurangi kualitas dari produk fotogrameteri yang dihasilkan. Penelitian ini bertujuan untuk menganalisa kontribusi sistem GNSS pada penentuan titik kontrol udara untuk metode fotogrametri berbasiskan UAV. Pengukuran GNSS frekuensi ganda pada sistem UAV di wilayah Jatinangor, Bandung dan Panglipuran Bali digunakan pada penelitian ini. Panjang baseline antara titik kontrol dan rover berkisar antara 350 hingga 900 m. Penentuan posisi titik kontrol udara berbasiskan GNSS menggunakan metode Post Processing Kinematic (PPK) dengan teknik pemecahan ambiguitas fase LAMBDA Fix and Hold. Pengolahan data GNSS dilakukan dengan menggunakan beberapa kombinasi frekuensi dan sistem GNSS. Evaluasi ketelitian hasil perataan berkas menggunakan titik kontrol udara pada setiap kombinasi frekuensi dan sistem GNSS dilakukan dengan memperhatikan nilai Root Mean Square Error (RMSE) pada 20 titik cek tanah atau Independent Check Points (ICP). Berdasarkan hasil tersebut, kombinasi gelombang L1 dan L2 menggunakan sistem GPS dan BeiDou idealnya digunakan untuk pemetaan skala besar menggunakan fotogrametri UAV. Selain itu, kombinasi data GPS dan Beidou frekuensi ganda memiliki tingkat ketelitian titik kontrol udara yang terbaik dibandingkan kombinasi yang lainnya. Selain itu, kombinasi GPS dan BeiDou menggunakan hanya gelombang L1 memiliki tingkat ketelitian yang sama dibandingkan dengan GPS menggunakan gelombang L1 dan L2.

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Unmanned Aerial Vehicle Surveying For Monitoring Road Construction Earthworks

The Baltic Journal of Road and Bridge Engineering ◽

10.7250/bjrbe.2019-14.430 ◽

2019 ◽

Vol 14 (1) ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Kalev Julge ◽

Artu Ellmann ◽

Romet Köök

Keyword(s):

Unmanned Aerial Vehicle ◽

Road Construction ◽

Point Clouds ◽

Satellite System ◽

Ground Control Points ◽

Surface Models ◽

Aerial Vehicle ◽

Global Navigation Satellite ◽

Sand And Gravel ◽

The Impact

Unmanned aerial vehicle photogrammetry is a surveying technique that enables generating point clouds, 3D surface models and orthophoto mosaics. These are based on photos captured with a camera placed on an unmanned aerial vehicle. Within the framework of this research, unmanned aerial vehicle photogrammetry surveys were carried out over a sand and gravel embankment with the aim of assessing the vertical accuracy of the derived surface models. Flight altitudes, ground control points and cameras were varied, and the impact of various factors on the results was monitored. In addition, the traditional real-time-kinematic Global Navigation Satellite System surveys were conducted for verifications. Surface models acquired by different methods were used to calculate volumes and compare the results with requirements set by Estonian Road Administration. It was found that with proper measuring techniques an accuracy of 5.7 cm for the heights were achieved.

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Automated Coregistration of Multisensor Orthophotos Generated from Unmanned Aerial Vehicle Platforms

Journal of Sensors ◽

10.1155/2019/2962734 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Youkyung Han ◽

Jaewan Choi ◽

Jinha Jung ◽

Anjin Chang ◽

Sungchan Oh ◽

...

Keyword(s):

Unmanned Aerial Vehicle ◽

Spatial Information ◽

Test Site ◽

Accurate Method ◽

Satellite System ◽

Phase Correlation ◽

Conjugate Points ◽

Acquisition Time ◽

Image Coregistration ◽

Aerial Vehicle

Image coregistration is a key preprocessing step to ensure the effective application of very-high-resolution (VHR) orthophotos generated from multisensor images acquired from unmanned aerial vehicle (UAV) platforms. The most accurate method to align an orthophoto is the installation of air-photo targets at a test site prior to flight image acquisition, and these targets were used as ground control points (GCPs) for georeferencing and georectification. However, there are time and cost limitations related to installing the targets and conducting field surveys on the targets during every flight. To address this problem, this paper presents an automated coregistration approach for orthophotos generated from VHR images acquired from multisensors mounted on UAV platforms. Spatial information from the orthophotos, provided by the global navigation satellite system (GNSS) at each image’s acquisition time, is used as ancillary information for phase correlation-based coregistration. A transformation function between the multisensor orthophotos is then estimated based on conjugate points (CPs), which are locally extracted over orthophotos using the phase correlation approach. Two multisensor datasets are constructed to evaluate the proposed approach. These visual and quantitative evaluations confirm the superiority of the proposed method.

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Spatial ETL for 3D Building Modelling Based on Unmanned Aerial Vehicle Data in Semi-Urban Areas

Remote Sensing ◽

10.3390/rs12121972 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1972 ◽

Cited By ~ 1

Author(s):

Urška Drešček ◽

Mojca Kosmatin Fras ◽

Jernej Tekavec ◽

Anka Lisec

Keyword(s):

Unmanned Aerial Vehicle ◽

Spatial Data ◽

Urban Areas ◽

Point Cloud ◽

Process Model ◽

Laser Scanning ◽

3D City Models ◽

Aerial Vehicle ◽

Spatial Etl ◽

City Models

This paper provides the innovative approach of using a spatial extract, transform, load (ETL) solution for 3D building modelling, based on an unmanned aerial vehicle (UAV) photogrammetric point cloud. The main objective of the paper is to present the holistic workflow for 3D building modelling, emphasising the benefits of using spatial ETL solutions for this purpose. Namely, despite the increasing demands for 3D city models and their geospatial applications, the generation of 3D city models is still challenging in the geospatial domain. Advanced geospatial technologies provide various possibilities for the mass acquisition of geospatial data that is further used for 3D city modelling, but there is a huge difference in the cost and quality of input data. While aerial photogrammetry and airborne laser scanning involve high costs, UAV photogrammetry has brought new opportunities, including for small and medium-sized companies, by providing a more flexible and low-cost source of spatial data for 3D modelling. In our data-driven approach, we use a spatial ETL solution to reconstruct a 3D building model from a dense image matching point cloud which was obtained beforehand from UAV imagery. The results are 3D building models in a semantic vector format consistent with the OGC CityGML standard, Level of Detail 2 (LOD2). The approach has been tested on selected buildings in a simple semi-urban area. We conclude that spatial ETL solutions can be efficiently used for 3D building modelling from UAV data, where the data process model developed allows the developer to easily control and manipulate each processing step.

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Nghiên cứu chế độ bay UAV trong khảo sát địa hình công trình dạng tuyến - ứng dụng cho đoạn đường đê Xuân Quan, Hà Nội

Tạp chí Khoa học Công nghệ Xây dựng (KHCNXD) - ĐHXD ◽

10.31814/stce.huce(nuce)2021-15(7v)-12 ◽

2021 ◽

Vol 15 (7V) ◽

pp. 131-142

Author(s):

Lương Ngọc Dũng ◽

Trần Đình Trọng ◽

Vũ Đình Chiều ◽

Bùi Duy Quỳnh ◽

Hà Thị Hằng ◽

...

Keyword(s):

Real Time ◽

Unmanned Aerial Vehicle ◽

Global Navigation Satellite System ◽

Satellite System ◽

Viet Nam ◽

Navigation Satellite ◽

Real Time Kinematic ◽

Aerial Vehicle ◽

Global Navigation ◽

Global Navigation Satellite

Giải pháp thành lập bản đồ địa hình bằng thiết bị bay không người lái (Unmanned Aerial Vehicle - UAV) đang ngày càng phổ biến ở Việt Nam. Đã có nhiều nghiên cứu chứng minh thiết bị UAV đảm bảo độ chính xác thành lập bản đồ địa hình tỷ lệ lớn, tuy nhiên chưa có các giải pháp cụ thể cho công trình đặc thù dạng tuyến. Mục tiêu nghiên cứu của bài báo là các chế độ bay phù hợp cho công tác khảo sát địa hình các công trình dạng tuyến. Đối tượng thực nghiệm, một đoạn đường bộ thuộc địa phận đê Xuân Quan, Hà Nội, được khảo sát bằng thiết bị UAV Phantom 4 Pro với các chế độ khác nhau trên các phần mềm điều khiển bay có sẵn. Kết quả thực nghiệm các chế độ bay được so sánh với kết quả đo định vị động thời gian thực (Global Navigation Satellite System/Real Time Kinematic - GNSS/RTK) để đánh giá độ chính xác. Nghiên cứu chỉ ra kiểu bay dải phủ trùm, đối với công trình dạng tuyến, thích hợp ở các giai đoạn thiết kế kỹ thuật và thiết kế thi công. Trong khi kiểu bay 2 dải đơn phù hợp và hiệu quả cho các quá trình quy hoạch, đánh giá sơ bộ công trình dạng tuyến.

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Validation and application of sequential unmanned aerial vehicle surveys to monitor the kinematics of a rapid rock glacier

10.5194/esurf-2021-8 ◽

2021 ◽

Author(s):

Sebastián Vivero ◽

Reynald Delaloye ◽

Christophe Lambiel

Keyword(s):

Unmanned Aerial Vehicle ◽

Environmental Changes ◽

Surface Expression ◽

Satellite System ◽

Surface Velocity ◽

European Alps ◽

Rock Glacier ◽

Monitoring Method ◽

Aerial Vehicle ◽

Global Navigation Satellite

Abstract. Accurately assessing landform evolution and quantifying rapid environmental changes are gaining importance in the context of monitoring techniques in alpine environments. In the European Alps, glaciers and rock glaciers are among the most characteristic cryospheric components bearing the most prolonged monitoring periods. This study introduces a rigorous procedure to quantify rock glacier kinematics and their associated uncertainty derived from sequential unmanned aerial vehicle (UAV) surveys. High-resolution digital elevation models (DEMs) and orthomosaics are derived from UAV image series combined with structure from motion (SfM) photogrammetry techniques. Multitemporal datasets are employed for measuring spatially continuous rock glacier kinematics using image matching algorithms. This procedure is tested on seven consecutive (from 2016 to 2019) UAV surveys of Tsarmine rock glacier, Valais Alps, Switzerland. The evaluation of superficial displacements was performed with simultaneous in-situ differential global navigation satellite system (GNSS) measurements. During the study period, the rock glacier doubled its overall frontal velocity, from around 5 m yr−1 between October 2016 and June 2017 to more than 10 m yr−1 between June and September 2019. Using the adequate UAV survey acquisition, processing, and validation steps, we almost achieved the same accuracy as the GNSS-derived velocities. Nevertheless, the proposed monitoring method provides accurate surface velocity fields values, which allow an enhanced description of the current rock glacier dynamics and its surface expression.

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Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

Remote Sensing ◽

10.3390/rs13010024 ◽

2020 ◽

Vol 13 (1) ◽

pp. 24

Author(s):

Yuanshuo Hao ◽

Faris Rafi Almay Widagdo ◽

Xin Liu ◽

Ying Quan ◽

Lihu Dong ◽

...

Keyword(s):

Unmanned Aerial Vehicle ◽

Random Effects ◽

Forest Inventory ◽

Point Cloud ◽

Laser Scanning ◽

Small Scale ◽

Individual Tree ◽

Scanning Angle ◽

Tree Diameter ◽

Aerial Vehicle

Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.

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